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wine regedit wine-breeze-dark.reg
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Made possible with this reddit post.
wine regedit wine-breeze-dark.reg
wine regedit wine-reset-theme.reg
| ;; ━━━━━━━━━━━━━━━━━━ | |
| ;; 作者: 李继刚 | |
| ;; 剑名: 商业结构 | |
| ;; 剑意: 看懂「公司」的结构形状 | |
| ;; 日期: 2026-01-21 | |
| ;; ━━━━━━━━━━━━━━━━━━ | |
| ** 【角色设定】 | |
| 你是一位系统战略分析师。你擅长透过表象(财报、新闻),洞察一个商业组织底层的能量运作逻辑。你认为万物皆为“结构”,而结构是在压力下由向心力与离心力动态平衡形成的“涡漩体”。 |
Here's an overview of my Factorio ship designs which can be built inexpensively.
See also the Design topics section below.
These ships are designed for use within the first four planets.
| ; Tera Term用 Dracula Theme 設定 | |
| ; https://draculatheme.com | |
| ; | |
| ; License: CC0 | |
| ; | |
| ; 以下の設定を、TERATERM.INI内の同じ設定と置き換えてください | |
| ; VTBoldColor, VTBlinkColor, URLColor の設定は適当なのでお好みに合わせて調整してください | |
| ; | |
| [Tera Term] | |
| ANSIColor=0,0,0,0, 1,255,110,103, 2,90,247,142, 3,244,249,157, 4,202,169,250, 5,255,146,208, 6,154,237,254, 7,230,230,230, 8,104,104,104, 9,255,85,85, 10,80,250,123, 11,241,250,140, 12,202,169,250, 13,255,121,198, 14,139,233,253, 15,191,191,191 |
| ANSIColor=0,7,54,66, 1,203,75,22, 2,88,110,117, 3,101,123,131, 4,131,148,150, 5,108,113,196, 6,147,161,161, 7,253,246,227, 8,0,43,54, ,220,50,47, 10,133,153,0, 11,181,137,0, 12,38,139,210, 13,211,54,130, 14,42,161,152, 15,238,232,213 | |
| ; Solarized Dark | |
| VTColor=131,148,150,0,43,54 | |
| VTBoldColor=147,161,161,7,54,66 | |
| VTBlinkColor=133,153,0,0,43,54 | |
| ;VTReverseColor=0,43,54,131,148,150 | |
| VTReverseColor=101,123,131,253,246,227 | |
| URLColor=181,137,0,0,43,54 |
A pattern for building personal knowledge bases using LLMs.
This is an idea file, it is designed to be copy pasted to your own LLM Agent (e.g. OpenAI Codex, Claude Code, OpenCode / Pi, or etc.). Its goal is to communicate the high level idea, but your agent will build out the specifics in collaboration with you.
Most people's experience with LLMs and documents looks like RAG: you upload a collection of files, the LLM retrieves relevant chunks at query time, and generates an answer. This works, but the LLM is rediscovering knowledge from scratch on every question. There's no accumulation. Ask a subtle question that requires synthesizing five documents, and the LLM has to find and piece together the relevant fragments every time. Nothing is built up. NotebookLM, ChatGPT file uploads, and most RAG systems work this way.
| name | japanese-tech-writing |
|---|---|
| description | 日本語の技術文書・書籍原稿の文章規範。段落と論証の構成(パラグラフライティング)、論証の厳密さ(ツッコミどころの除去)、読み手の負荷の管理、視点と語り、演出の抑制、LLM っぽい空句の禁止、翻訳調の比喩と擬人化の禁止(「運ぶ」「効く」「開かれた問い」など)、冗長の排除を定める。日本語で技術書の章、草稿、記事、解説文を書くとき、または推敲・リライトするときに使用する。 |
| license | Unlicense(https://gist.github.com/k16shikano/67625f2a7d96e3bbdfae8d571a936063) |
日本語で技術的な原稿(書籍の章、記事、解説文)を書く・推敲するときは、以下の規範に従う。
| /* | |
| This script, when used with Google Apps Scripts, will delete 400 emails and | |
| can be triggered to run every few minutes without user interaction enabling you | |
| to bulk delete email in Gmail without getting the #793 error from Gmail. | |
| Google returns a maximum of 500 email threads in a single API call. | |
| This script fetches 400 threads in case 500 threads is causing timeouts | |
| Configure the search query in the code below to match the type of emails | |
| you want to delete |
A collection of information gathered from various resources into a single document to be able to program for Amstrad CPC 464/6128 machines.
CPC has the following components on the mainboard: